Analyte monitoring system and method

ABSTRACT

Devices and methods for monitoring an analyte are provided. Embodiments include continuous analyte sensors having a high degree of accuracy.

RELATED APPLICATIONS

This application claims priority under 35 USC § 119 to ProvisionalApplication No. 60/804,170 filed Jun. 7, 2006 entitled “AnalyteMonitoring”, and to Provisional Application No. 60/804,169 filed Jun. 7,2006 entitled “Analyte Monitoring System” the disclosure of each ofwhich are incorporated in their entirety by reference for all purposes

BACKGROUND OF THE INVENTION

The association of chronic hyperglycemia and the devastating long-termcomplications of diabetes was clearly established by the DiabetesControl and Complication Trial (DCCT) (The Diabetes Control andComplications Trial Research Group. “The effect of intensive treatmentof diabetes on the development and progression of long-termcomplications of insulin-dependent diabetes mellitus” N Engl J Med 329:978-986, 1993; Santiago J V “Lessons from the Diabetes Control andComplications Trial” Diabetes 1993, 42: 1549-1554).

The DCCT found that in patients receiving intensive insulin therapy,there was a reduced risk of 76% for diabetic retinopathy, 50% fordiabetic nephropathy and 60% for diabetic neuropathy. The long-termbenefits of tight glycemic control have been further substantiated bythe Epidemiology of Diabetes Interventions and Complications study whichfound over a 50% reduced risk of macrovascular disease as a result ofintensive insulin therapy (The Diabetes Control and ComplicationsTrial/Epidemiology of Diabetes Intervention and Complication (DCCT/EDIC)Study Group, “Intensive diabetes treatment and cardiovascular disease inpatients with type 1 diabetes”, 353, 2643-2653, 2005).

However, the DCCT found that patients receiving intensive insulintherapy were at a threefold increased risk of severe hypoglycemia.Patients adhering to intensive insulin therapy regimens were found tohave lowered thresholds for activation of neurogenic warning systems andconsequently were at increased risk for more severe hypoglycemic events.(Amiel S A, Tamborlane W V, Simonson D C, Sherwin R S., “Defectiveglucose counterregulation after strict glycemic control ofinsulin-dependent diabetes mellitus.” N Engl J Med. 1987 28;316(22):1376-83).

The increased risk of hypoglycemia and the fear associated withpatients' perception of that risk has been cited as the leading obstaclefor patients to achieve the targeted glycemic levels (Cryer P E.“Hypoglycaemia: The limiting factor in the glycemic management of type Iand type II diabetes” Diabetologia, 2002, 45: 937-948). In addition tothe problem of chronic hyperglycemia contributing to long-termcomplications and the problem of acute iatrogenic hypoglycemiacontributing to short-term complications, recent research suggests thattransient episodes of hyperglycemia can lead to a wide range of seriousmedical problems besides previously identified microvascularcomplications as well as macrovascular complications such as increasedrisk for heart disease. (Haffner S “The importance of postprandialhyperglycemia in development of cardiovascular disease in people withdiabetes” International Journal of Clinical Practice, 2001, Supplement123: 24-26; Hanefeld M: “Postprandial hyperglycemia: noxious effects onthe vessel wall” International Journal of Clinical Practice, 2002,Supplement 129: 45-50).

Additional research has found that glycemic variation and the associatedoxidative stress may be implicated in the pathogenesis of diabeticcomplications (Hirsh I B, Brownlee M “Should minimal blood glucosevariability become the gold standard of glycemic control?” J of Diabetesand Its Complications, 2005, 19: 178-181; Monnier, L., Mas, E., Ginet,C., Michel, F., Villon L, Cristol J-P, and Collette C, “Activation ofoxidative stress by acute glucose fluctuations compared with sustainedchronic hyperglycemia in patients with type 2 diabetes”. JAMA 2006, 295,1681-1687). Glycemic variation has also been identified as a possibleexplanation for the increased prevalence of depression in both type 1and type 2 diabetes (Van der Does F E. De Neeling J N, Snoek F J,Kostense P J, Grootenhuis P A, Bouter L M, and R J Heine: Symptoms andwell-being in relation to glycemic control in type II diabetes DiabetesCare, 1996, 19: 204-210; De Sonnaville J J. Snoek F J. Colly L P.Deville W. Wijkel D. Heine R J: “Well-being and symptoms in relation toinsulin therapy in type 2 diabetes” Diabetes Care, 1998, 21:919-24; CoxD J, Gonder-Frederick L A, McCall A, et al. “The effects of glucosefluctuation on cognitive function and QOL: the functional costs ofhypoglycaemia and hyperglycaemia among adults with type 1 or type 2diabetes” International Journal of Clinical Practice, 2002, Supplement129: 20-26).

The potential benefits of continuous glucose monitoring have beenrecognized by numerous researchers in the field (Skyler J S “Theeconomic burden of diabetes and the benefits of improved glycemiccontrol: the potential role of a continuous glucose monitoring system”Diabetes Technol Ther 2 (Suppl 1): S7-S12, 2000; Tansey M J, Beck R W,Buckingham B A, Mauras N, Fiallo-Scharer R, Xing D, Kollman C,Tamborlane W V, Ruedy K J, “Accuracy of the modified Continuous GlucoseMonitoring System (CGMS) sensor in an outpatient setting: results from adiabetes research in children network (DirecNet) study.” Diab. Tech.Ther. 7(1):109-14, 2005; Klonoff, D C: “Continuous glucose monitoring:Roadmap for 21st century diabetes therapy” Diabetes Care, 2005, 28:1231:1239). Accurate and reliable real-time continuous glucosemonitoring devices have the ability to alert patients of high or lowblood sugars that might otherwise be undetected by episodic capillaryblood glucose measurements.

Continuous glucose monitors have the potential to permit more successfuladherence to intensive insulin therapy regimens and also to enablepatients to reduce the frequency and extent of glycemic fluctuations.However, the development of this technology has proceeded more slowlythan anticipated. For example, two recent comprehensive reviews ofdecades of research in the field cited the lack of accuracy andreliability as the major factor limiting the acceptance of this newtechnology as well as the development of an artificial pancreas (Chia,C. W. and Saudek, C. D., “Glucose sensors: toward closed loop insulindelivery” Endocrinol. Metab. Clin. N. Am., 33, 174-195, 2004; Hovorka,R. “Continuous glucose monitoring and closed-loop systems” Diabet. Med.23, 1-12, 2006).

As continuous analyte monitoring becomes more prevalent, of use arecontinuous analyte sensors and systems that are accurate to such a highdegree that confirmatory analyte measurement are not needed to verifythe continuous sensing measurements, e.g., prior to a user relying onthe continuous measurements. Also of interest are such sensors that workin concert with a drug delivery device.

SUMMARY OF THE INVENTION

Generally, the present disclosure relates to methods and devices formonitoring of the level of an analyte using a continuous and/orautomatic in vivo monitoring analyte sensor. Embodiments include sensorsin which at least a portion of the sensor is adapted to be positionedbeneath the skin of a user and which are adapted for providingclinically accurate analyte data, i.e., data with accuracy sufficient sothat a user may confidently rely on the sensor results, e.g., to managea disease condition and/or make a healthcare decision based thereon.Accordingly, sensors capable of providing clinically accurate (i.e.,clinically relevant) analyte information to a user are provided.

Embodiments include continuous analyte monitoring systems that do notrequire additional analyte information obtained by a second systemand/or sensor to confirm the results reported by the continuous sensingsystem.

Embodiments also include high accuracy continuous analyte sensors andsystems with drug delivery systems e.g., insulin pumps, or the like. Acommunication link (e.g., by cable or wirelessly such as by infrared(IR) or RF link or the like) may be provided for transfer of data fromthe sensor to the drug delivery device. The drug delivery device mayinclude a processor to determine the amount of drug to be deliveredusing sensor data, and may deliver such drug automatically or after userdirection to do so.

Also provided are methods of analyte monitoring using highly accuratecontinuous analyte sensors.

These and other objects, features and advantages of the presentdisclosure will become more fully apparent from the following detaileddescription of the embodiments, the appended claims and the accompanyingdrawings.

BRIEF DESCRIPTIONS OF THE DRAWINGS

The figures shown herein are not necessarily drawn to scale, with somecomponents and features being exaggerated for clarity. Each of thefigures diagrammatically illustrates aspects of the present disclosure.Of these:

FIG. 1 is a block diagram of one embodiment of a highly accuratecontinuous glucose monitoring system such as Freestyle Navigator® systemusing a subcutaneously implantable analyte sensor, according to oneembodiment of the present disclosure;

FIG. 2 shows five day accuracy data for the monitoring system of FIG. 1(arm and abdomen) and 50 hours of YSI venous sampling in one embodiment;

FIG. 3 shows a Clarke error grid for the continuous monitoring system ofFIG. 1 in one embodiment;

FIG. 4A shows a view (four hour duration) of profile plot centeredglucose challenge, and FIG. 4B shows a view (four hour duration) ofprofile plot centered insulin challenge;

FIG. 5 shows rate of change histogram showing underlying rate of changeat high resolution (in units of 0.25 mg/dL/min) and in units of thecontinuous monitoring system of FIG. 1 receiver trend arrows (1.0mg/dL/min);

FIG. 6 shows a Clarke error grid for YSI rates of change between −1 to 1mg/dL/min;

FIG. 7 shows the Clarke error grid from a high accurate continuousglucose monitoring system user study; and

FIG. 8 illustrates the time spent in hypoglycemic, euglycemic, andhyperglycemic ranges for type 1 and 2 subjects in the blinded andunblinded phases of the study described in conjunction with FIG. 7.

DETAILED DESCRIPTION

Before the various embodiments of the present disclosure is described,it is to be understood that this disclosure is not limited to particularembodiments described, as such may, of course, vary. It is also to beunderstood that the terminology used herein is for the purpose ofdescribing particular embodiments only, and is not intended to belimiting, since the scope of the present disclosure will be limited onlyby the appended claims.

Where a range of values is provided, it is understood that eachintervening value, to the tenth of the unit of the lower limit unlessthe context clearly dictates otherwise, between the upper and lowerlimit of that range and any other stated or intervening value in thatstated range, is encompassed within the present disclosure. The upperand lower limits of these smaller ranges may independently be includedin the smaller ranges is also encompassed within the present disclosure,subject to any specifically excluded limit in the stated range. Wherethe stated range includes one or both of the limits, ranges excludingeither or both of those included limits are also included in the presentdisclosure.

Unless defined otherwise, all technical and scientific terms used hereinhave the same meaning as commonly understood by one of ordinary skill inthe art to which this present disclosure belongs. Although any methodsand materials similar or equivalent to those described herein can alsobe used in the practice or testing of various embodiments of the presentdisclosure, exemplary methods and materials are now described. Allpublications mentioned herein are incorporated herein by reference todisclose and describe the methods and/or materials in connection withwhich the publications are cited.

It must be noted that as used herein and in the appended claims, thesingular forms “a”, “an”, and “the” include plural referents unless thecontext clearly dictates otherwise.

The publications discussed herein are provided solely for theirdisclosure prior to the filing date of the present application. Nothingherein is to be construed as an admission that the present disclosure isnot entitled to antedate such publication by virtue of prior invention.Further, the dates of publication provided may be different from theactual publication dates which may need to be independently confirmed.

As will be apparent to those of skill in the art upon reading thisdisclosure, each of the individual embodiments described and illustratedherein has discrete components and features which may be readilyseparated from or combined with the features of any of the other severalembodiments without departing from the scope or spirit of the presentdisclosure.

The present disclosure is applicable to analyte monitoring systems usinga sensor—at least a portion of which is positioned beneath the skin ofthe user, for the in vivo determination of a concentration of ananalyte, such as glucose, lactate, and the like, in a body fluid. Thesensor may be, for example, subcutaneously positioned in a patient forthe continuous or periodic monitoring an analyte in a patient'sinterstitial fluid. This may be used to infer the glucose level in thepatient's bloodstream. The sensors of the subject disclosure alsoinclude in vivo analyte sensors for insertion into a vein, artery, orother portion of the body containing fluid. A sensor of the subjectdisclosure may be configured for monitoring the level of the analyteover a time period which may range from hours, days, weeks, or longer,as described in greater detail below.

More specifically, FIG. 1 illustrates a data monitoring and managementsystem such as, for example, analyte (e.g., glucose) monitoring system100, in accordance with one embodiment of the present disclosure. Thesubject disclosure is further described primarily with respect to aglucose monitoring system for convenience and such description is in noway intended to limit the scope of the present disclosure. It is to beunderstood that the analyte monitoring system may be configured tomonitor a variety of analytes. Analytes that may be monitored include,for example, acetyl choline, amylase, bilirubin, cholesterol, chorionicgonadotropin, creatine kinase (e.g., CK-MB), creatine, DNA,fructosamine, glucose, glutamine, growth hormones, hormones, ketones,lactate, peroxide, prostate-specific antigen, prothrombin, RNA, thyroidstimulating hormone, and troponin, and the like. The concentration ofdrugs, such as, for example, antibiotics (e.g., gentamicin, vancomycin,and the like), digitoxin, digoxin, drugs of abuse, theophylline, andwarfarin, and the like, may also be monitored.

The analyte monitoring system 100 includes a highly accurate sensor 101,a transmitter unit 102 coupled to the sensor 101, and a receiver unit104 which is configured to communicate with the transmitter unit 102 viaa communication link 103. The receiver unit 104 may be furtherconfigured to transmit data to a data processing terminal 105 forevaluating the data received by the receiver unit 104. Moreover, thedata processing terminal in one embodiment may be configured to receivedata directly from the transmitter unit 102 via a communication link 106which may optionally be configured for bi-directional communication.Some or all of the various components may be separate components, orsome or all may be integrated into a single unit.

Only one sensor 101, transmitter unit 102, receiver unit 104,communication link 103, and data processing terminal 105 are shown inthe embodiment of the analyte monitoring system 100 illustrated inFIG. 1. However, it will be appreciated by one of ordinary skill in theart that the analyte monitoring system 100 may include one or moresensor 101, transmitter unit 102, receiver unit 104, communication link103, and data processing terminal 105. Moreover, within the scope of thepresent disclosure, the analyte monitoring system 100 may be acontinuous monitoring system, or semi-continuous, or a discretemonitoring system. In a multi-component environment, each device isconfigured to be uniquely identified by each of the other devices in thesystem so that communication conflict is readily resolved between thevarious components within the analyte monitoring system 100.

In one embodiment of the present disclosure, the sensor 101 isphysically positioned in or on the body of a user whose analyte level isbeing monitored. The sensor 101 may be configured to continuously samplethe analyte level of the user and convert the sampled analyte level intoa corresponding data signal for transmission by the transmitter unit102. In one embodiment, the transmitter unit 102 is coupled to, e.g.,mounted on, the sensor 101 so that both devices are positioned on theuser's body. The transmitter unit 102 performs data processing such asfiltering and encoding on data signals, each of which corresponds to asampled analyte level of the user, for transmission to the receiver unit104 via the communication link 103.

In one embodiment, the analyte monitoring system 100 is configured as aone-way RF communication path from the transmitter unit 102 to thereceiver unit 104. In such embodiment, the transmitter unit 102transmits the sampled data signals received from the sensor 101 withoutacknowledgement from the receiver unit 104 that the transmitted sampleddata signals have been received. For example, the transmitter unit 102may be configured to transmit the encoded sampled data signals at afixed rate (e.g., at one minute intervals) after the completion of theinitial power on procedure. Likewise, the receiver unit 104 may beconfigured to detect such transmitted encoded sampled data signals atpredetermined time intervals. Alternatively, the analyte monitoringsystem 100 may be configured with a bi-directional RF (or otherwise)communication between the transmitter unit 102 and the receiver unit104.

Additionally, in one aspect, the receiver unit 104 may include twosections. The first section is an analog interface section that isconfigured to communicate with the transmitter unit 102 via thecommunication link 103. In one embodiment, the analog interface sectionmay include an RF receiver and an antenna for receiving and amplifyingthe data signals from the transmitter unit 102, which are thereafter,demodulated with a local oscillator and filtered through a band-passfilter. The second section of the receiver unit 104 is a data processingsection which is configured to process the data signals received fromthe transmitter unit 102 such as by performing data decoding, errordetection and correction, data clock generation, and data bit recovery.

In certain embodiments, in operation, the receiver unit 104 isconfigured to detect the presence of the transmitter unit 102 within itsrange based on, for example, the strength of the detected data signalsreceived from the transmitter unit 102 or a predetermined transmitteridentification information. Upon successful synchronization with thecorresponding transmitter unit 102, the receiver unit 104 is configuredto begin receiving from the transmitter unit 102 data signalscorresponding to the user's detected analyte level. More specifically,the receiver unit 104 in one embodiment is configured to performsynchronized time hopping with the corresponding synchronizedtransmitter unit 102 via the communication link 103 to obtain the user'sdetected analyte level.

Referring again to FIG. 1, the data processing terminal 105 may includea personal computer, a portable computer such as a laptop or a handhelddevice (e.g., personal digital assistants (PDAs)), and the like, each ofwhich may be configured for data communication with the receiver via awired or a wireless connection. Additionally, the data processingterminal 105 may further be connected to a data network (not shown) forstoring, retrieving and updating data corresponding to the detectedanalyte level of the user.

Within the scope of the present disclosure, the data processing terminal105 may include an infusion device such as an insulin infusion pump orthe like, which may be configured to administer insulin to patients, andwhich may be configured to communicate with the receiver unit 104 forreceiving, among others, the measured analyte level. Alternatively, thereceiver unit 104 may be configured to integrate an infusion devicetherein so that the receiver unit 104 is configured to administerinsulin therapy to patients, for example, for administering andmodifying basal profiles, as well as for determining appropriate bolusesfor administration based on, among others, the detected analyte levelsreceived from the transmitter unit 102.

Additionally, the transmitter unit 102, the receiver unit 104 and thedata processing terminal 105 may each be configured for bi-directionalwireless communication such that each of the transmitter unit 102, thereceiver unit 104 and the data processing terminal 105 may be configuredto communicate (that is, transmit data to and receive data from) witheach other via the wireless communication link 103. More specifically,the data processing terminal 105 may in one embodiment be configured toreceive data directly from the transmitter unit 102 via thecommunication link 106, where the communication link 106, as describedabove, may be configured for bi-directional communication.

In this embodiment, the data processing terminal 105 which may includean insulin pump, may be configured to receive the analyte signals fromthe transmitter unit 102, and thus, incorporate the functions of thereceiver 103 including data processing for managing the patient'sinsulin therapy and analyte monitoring. In one embodiment, thecommunication link 103 may include one or more of an RF communicationprotocol, an infrared communication protocol, a Bluetooth enabledcommunication protocol, an 802.11x wireless communication protocol, oran equivalent wireless communication protocol which would allow secure,wireless communication of several units (for example, per HIPPArequirements) while avoiding potential data collision and interference.

Continuous Glucose Monitoring Sensors and Systems

As described above, the various embodiments of the present disclosurerelate to continuous analyte sensors and systems having a high degree ofaccuracy, e.g., as demonstrated by a Clark Error Grid, Parks Error Grid,Continuous Glucose Error Grid, MARD analysis, and the like. The highdegree of accuracy permits a user to rely on the results of the sensorwithout the need to confirm sensor results. In certain embodiments, thesensors have at least about 80% of its paired data points within zone Aof one or more of the Clark Error Grid, the Consensus Error Grid, or theContinuous Glucose Error Grid Analysis, e.g., at least about 85% of itspaired data points within zone A of one or more of the Clark Error Grid,the Consensus Error Grid, or the Continuous Glucose Error Grid Analysis,e.g., at least about 90% of its paired data points within zone A of oneor more of the Clark Error Grid, the Consensus Error Grid, or theContinuous Glucose Error Grid Analysis, e.g., at least about 95% of itspaired data points within zone A of one or more of the Clark Error Grid,the Consensus Error Grid, or the Continuous Glucose Error Grid Analysis.

In certain embodiments, a sensor may have about 80% or greater, e.g.,85% or greater, e.g., 90% or greater of its paired data points withinzone A of the Clark Error Grid, and 80% or greater, e.g., 85% orgreater, e.g., 90% or greater, of its paired data points within zone Aof the Consensus Error Grid.

The sensors are continuous analyte monitoring sensors. The sensors areadapted to continuously or periodically monitor analyte levels for aperiod of time, e.g., usually at least about 24 hours, e.g., about 1 dayto about 30 days, e.g., about 3 days to about 7 days, e.g., a 5 daysensor or 7 day sensor.

Embodiments of the clinically accurate continuous glucose monitoringsystems of the present disclosure include four components: a small,miniaturized analyte sensor element (which may be an electrochemical oroptical sensor) for placement in the subcutaneous adipose tissue in thearm or abdomen (or elsewhere); a disposable sensor delivery unitcontaining a spring-loaded sharp for mechanical insertion of the sensorinto the tissue and a sensor support mount; a transmitter (e.g.,wireless transmitter) which connects to the sensor support mount on theskin surface and to the inserted electrochemical sensor; and a hand-heldreceiver device for communication (e.g., wireless) with the transmitterand for the communication (e.g., audio and/or visual display) of thecontinuous glucose values to the user. The system may also include adata management system in which information from the receiver (and/ortransmitter) is forwarded (e.g., wirelessly or otherwise) to a datamanagement system such as a personal computer (“PC”), personal digitalassistant (“PDA”), telephone, facsimile machine, drug delivery device(e.g., internal or external insulin pump) or the like.

Embodiments of the sensors of the present disclosure vary, but in allembodiments have a high degree of accuracy. In other words, the sensors'accuracy enables a user of the system to solely and confidently rely onthe sensors' results that are reportable to the user, e.g., to manage adisease state such as diabetes or the like, make healthcare decisions(e.g., insulin delivery, meals, exercise, etc.). In this manner,adjunctive measurements are not required to confirm the readings of thehighly accurate sensors of the present disclosure, thereby eliminatingburdensome and painful fingersticks required for testing analyte usingconventional blood analyte monitoring systems such as blood glucose teststrips and the like, used for such confirmations.

In certain embodiments a sensor is adapted to be wholly or partiallypositioned beneath the skin surface of a user. A sensor may be atranscutaneous sensor in which a portion of the sensor is configured tobe positioned beneath a skin surface and portion is configured to bepositioned above the skin surface. In many embodiments at least aportion of the sensor is configured to be inserted into the subcutaneousadipose tissue. Sensors may vary in size, where in certain embodiments asensor may be about 5.5 mm long, about 600 microns wide and about 250microns thick. Sensors having different lengths and/or widths and/orthicknesses are also encompassed by the present disclosure. The sensorsare configured to accurately measure an analyte, e.g., glucoseconcentration in the interstitial fluid, which has correlates with bloodglucose. The sensor is typically provided to a user as a sterile,single-use disposable element.

The sensors may be configured to continuously monitor analyte levels ofa user for a period of time. In certain embodiments, the period of timeranges from about 1 day to about 30 days, e.g., from about 3 days toabout 7 days, where in certain embodiments a sensor may configured forup to about five days of continuous use. A system may include two ormore sensors, which may be temporally overlapped for a certain period ofusage time, thereby extending the amount of time of continuous sensingand/or doing away with any time gaps that may result from removing afirst sensor and inserting a second. Furthermore, a sensor may becalibrated from a previous sensor in certain embodiments.

The glucose measurement is made using sensing chemistry. Sensingchemistry may include an enzyme and may include a mediator. In certainembodiments, the sensing chemistry is a modified glucose oxidasepolymeric matrix with an osmium dopant in the supporting polymer matrix.The sensing chemistry (also referred to as the “transduction chemistry”)used in the sensors of the present disclosure permits detection ofsignal, e.g., a nanoampere electrical current from the reaction with anapplied potential, such as of only about 40 mV.

More specifically, in one embodiment, the sensor includes at least oneworking electrode formed on a substrate. The sensor may also include atleast one counter electrode (or counter/reference electrode) and/or atleast one reference electrode. The counter electrode and/or referenceelectrode may be formed on the substrate or may be separate units. Forexample, the counter electrode and/or reference electrode may be formedon a second substrate which is also implanted in the patient or, forsome embodiments of the implantable sensors, the counter electrodeand/or reference electrode may be placed on the skin of the patient withthe working electrode or electrodes being implanted into the patient.

The working electrode or electrodes are formed using conductive tracesdisposed on the substrate. The counter electrode and/or referenceelectrode, as well as other optional portions of the sensor, such as atemperature probe, may also be formed using conductive traces disposedon the substrate. These conductive traces may be formed over a smoothsurface of the substrate or within channels formed by, for example,embossing, indenting or otherwise creating a depression in thesubstrate.

A sensing layer is often formed proximate to or on at least one of theworking electrodes to facilitate the electrochemical detection of theanalyte and the determination of its level in the sample fluid,particularly if the analyte can not be electrolyzed at a desired rateand/or with a desired specificity on a bare electrode. The sensing layermay include an electron transfer agent to transfer electrons directly orindirectly between the analyte and the working electrode. The sensinglayer may also contain a catalyst to catalyze a reaction of the analyte.The components of the sensing layer may be in a fluid or gel that isproximate to or in contact with the working electrode. Alternatively,the components of the sensing layer may be disposed in a polymeric orsol-gel matrix that is proximate to or on the working electrode. In oneaspect, the components of the sensing layer are non-leachably disposedwithin the sensor. Further, the components of the sensor are immobilizedwithin the sensor.

In addition to the electrodes and the sensing layer, the sensor may alsoinclude a temperature probe, a mass transport limiting layer, abiocompatible layer, and/or other optional components, as describedbelow. Each of these items enhances the functioning of and/or resultsfrom the sensor, as discussed below.

The Substrate

The substrate may be formed using a variety of non-conducting materials,including, for example, polymeric or plastic materials and ceramicmaterials. Suitable materials for a particular sensor may be determined,at least in part, based on the desired use of the sensor and propertiesof the materials.

In some embodiments, the substrate is flexible. In other embodiments,the sensors are made using a relatively rigid substrate to, for example,provide structural support against bending or breaking.

Conductive Traces

At least one conductive trace is formed on the substrate for use inconstructing a working electrode. In addition, other conductive tracesmay be formed on the substrate for use as electrodes (e.g., additionalworking electrodes, as well as counter, counter/reference, and/orreference electrodes) and other components, such as a temperature probe.The conductive traces may be formed on the substrate by a variety oftechniques, including, for example, photolithography, screen printing,or other impact or non-impact printing techniques. The conductive tracesmay also be formed by carbonizing conductive traces in an organic (e.g.,polymeric or plastic) substrate using a laser.

The conductive traces are typically formed using a conductive material56 such as carbon (e.g., graphite), a conductive polymer, a metal oralloy (e.g., gold or gold alloy), or a metallic compound (e.g.,ruthenium dioxide or titanium dioxide). The formation of films ofcarbon, conductive polymer, metal, alloy, or metallic compound arewell-known and include, for example, chemical vapor deposition (CVD),physical vapor deposition, sputtering, reactive sputtering, printing,coating, and painting.

In addition to the particles of carbon, metal, alloy, or metalliccompound, the conductive ink may also contain a binder. The binder mayoptionally be cured to further bind the conductive material within thechannel and/or on the substrate.

Suitable redox couples for binding to the conductive material of thereference electrode include, for example, redox polymers (e.g., polymershaving multiple redox centers.). In one aspect, the reference electrodesurface may be non-corroding so that an erroneous potential is notmeasured. Examples of conductive materials include less corrosivemetals, such as gold and palladium, and may include non-corrosivematerials including non-metallic conductors, such as carbon andconducting polymers. A redox polymer can be adsorbed on or covalentlybound to the conductive material of the reference electrode, such as acarbon surface of a conductive trace. Non-polymeric redox couples can besimilarly bound to carbon or gold surfaces.

A variety of methods may be used to immobilize a redox polymer on anelectrode surface. One method is adsorptive immobilization. This methodis particularly useful for redox polymers with relatively high molecularweights. The molecular weight of a polymer may be increased, forexample, by cross-linking.

Another method for immobilizing the redox polymer includes thefunctionalization of the electrode surface and then the chemicalbonding, often covalently, of the redox polymer to the functional groupson the electrode surface.

Sensing Layer

Some analytes, such as oxygen, can be directly electrooxidized orelectroreduced on the working electrode. Other analytes, such as glucoseand lactate, require the presence of at least one electron transferagent and/or at least one catalyst to facilitate the electrooxidation orelectroreduction of the analyte. Catalysts may also be used for thoseanalyte, such as oxygen, that can be directly electrooxidized orelectroreduced on the working electrode. For these analytes, eachworking electrode has a sensing layer formed proximate to or on aworking surface of the working electrode. Typically, the sensing layeris formed near or on only a small portion of the working electrode,often near a tip of the sensor. This limits the amount of materialneeded to form the sensor and places the sensing layer 64 in the bestposition for contact with the analyte-containing fluid (e.g., a bodyfluid, sample fluid, or carrier fluid).

Electron Transfer Agent

In many embodiments, the sensing layer contains one or more electrontransfer agents in contact with the conductive material of the workingelectrode. In some embodiments of the present disclosure, there islittle or no leaching of the electron transfer agent away from theworking electrode during the period in which the sensor is implanted inthe patient. A diffusing or leachable (i.e., releasable) electrontransfer agent often diffuses into the analyte-containing fluid, therebyreducing the effectiveness of the electrode by reducing the sensitivityof the sensor over time.

In some embodiments of the present disclosure, to prevent leaching, theelectron transfer agents are bound or otherwise immobilized on theworking electrode or between or within one or more membranes or filmsdisposed over the working electrode. The electron transfer agent may beimmobilized on the working electrode using, for example, a polymeric orsol-gel immobilization technique. Alternatively, the electron transferagent may be chemically (e.g., ionically, covalently, or coordinatively)bound to the working electrode, either directly or indirectly throughanother molecule, such as a polymer, that is in turn bound to theworking electrode.

In general, electron transfer agents may be electroreducible andelectrooxidizable ions or molecules having redox potentials that are afew hundred millivolts above or below the redox potential of thestandard calomel electrode (SCE). Further, the electron transfer agentsare not more reducing than about −150 mV and not more oxidizing thanabout +400 mV versus SCE.

Catalyst

The sensing layer may also include a catalyst which is capable ofcatalyzing a reaction of the analyte. The catalyst may also, in someembodiments, act as an electron transfer agent. One example of asuitable catalyst is an enzyme which catalyzes a reaction of theanalyte. In one aspect, the catalyst is non-leachably disposed on thesensor, whether the catalyst is part of a solid sensing layer in thesensor or solvated in a fluid within the sensing layer. In a furtheraspect, the catalyst is immobilized within the sensor (e.g., on theelectrode and/or within or between a membrane or film) to preventunwanted leaching of the catalyst away from the working electrode andinto the patient. This may be accomplished, for example, by attachingthe catalyst to a polymer, cross linking the catalyst with anotherelectron transfer agent (which can be polymeric), and/or providing oneor more barrier membranes or films with pore sizes smaller than thecatalyst.

Biocompatible Layer

An optional film layer is formed over at least that portion of thesensor which is subcutaneously inserted into the patient. This optionalfilm layer may serve one or more functions. The film layer prevents thepenetration of large biomolecules into the electrodes. This isaccomplished by using a film layer having a pore size that is smallerthan the biomolecules that are to be excluded. Such biomolecules mayfoul the electrodes and/or the sensing layer thereby reducing theeffectiveness of the sensor and altering the expected signal amplitudefor a given analyte concentration. The fouling of the working electrodesmay also decrease the effective life of the sensor. The biocompatiblelayer may also prevent protein adhesion to the sensor, formation ofblood clots, and other undesirable interactions between the sensor andbody.

Interferent-Eliminating Layer

An interferent-eliminating layer may be included in the sensor. Theinterferent-eliminating layer may be incorporated in the biocompatiblelayer or in the mass transport limiting layer (described below) or maybe a separate layer. Interferents are molecules or other species thatare electroreduced or electrooxidized at the electrode, either directlyor via an electron transfer agent, to produce a false signal. In oneembodiment, a film or membrane prevents the penetration of one or moreinterferents into the region around the working electrodes. In oneaspect, this type of interferent-eliminating layer is much lesspermeable to one or more of the interferents than to the analyte.

Mass Transport Limiting Layer

A mass transport limiting layer may be included with the sensor to actas a diffusion-limiting barrier to reduce the rate of mass transport ofthe analyte, for example, glucose or lactate, into the region around theworking electrodes. By limiting the diffusion of the analyte, the steadystate concentration of the analyte in the proximity of the workingelectrode (which is proportional to the concentration of the analyte inthe body or sample fluid) can be reduced. This extends the upper rangeof analyte concentrations that can still be accurately measured and mayalso expand the range in which the current increases approximatelylinearly with the level of the analyte. Particularly useful materialsfor the film layer are membranes that do not swell in theanalyte-containing fluid that the sensor tests.

Suitable membranes include 3 to 20,000 nm diameter pores. Membraneshaving 5 to 500 nm diameter pores with well-defined, uniform pore sizesand high aspect ratios may be used. In one embodiment, the aspect ratioof the pores may be two or greater, or in one aspect five or greater.

Embodiments of the system include a receiver that includes both thesignal processing algorithms and the user interface system for operationof the system and display of the results—although one or both may beincorporated wholly or partially into the transmitter of the system. Inoperation, the glucose display on the main screen of the receiver isupdated during a predetermined time period, e.g., about once a minute orthe like, and gives the instantaneous continuous glucose value. Alsoprovided may be the direction and/or rate of change averaged over apredetermined period of time, e.g., the preceding fifteen minutes, orthe like. The direction may be communicated using any suitable audioand/or visual indicator(s). For example, direction may be displayed withtrend arrows that give quantitative ranges of the rate of change inunits of about 1 mg/dL/min from about −2 mg/dL/min to about +2mg/dL/min. The receiver may also include threshold and/or projectedwarnings—audible and/or visual warnings. These may be settable at thefactory and/or by the user to different glucose levels to providewarnings of actual and impending hypo- or hyperglycemia. Other warningsmay also be included, e.g., battery level, and the like.Time-to-calibrate indicators may also be included.

The system may also include a blood glucose (“BG”) meter for use withglucose test strips which may be used for calibration of the continuousglucose sensor, but as noted above, is not needed to confirm thecontinuous sensor results. The BG meter may be a separate, thoughconnectable component, or may be integrated into the receiver as asingle unitary device. For example, the receiver may include a teststrip port and a processor to process a reading from the test strip. Thebuilt-in blood glucose meter eliminates the possibility of transcriptionerrors during sensor calibration and also provides the user with abackup glucose meter system.

The continuous glucose systems of the present disclosure may becalibrated according to a predetermined calibration schedule. In certainembodiments, this schedule may be limited to factory-only calibration.However in certain embodiments, the calibration schedule may includecalibrations by the user. For example, over the period of use of thesystem, it may be calibrated from about 0 to about 10 times, e.g., fromabout 1 to about 5 times, e.g., about 4 times. An exemplary calibrationschedule may include calibration 4 times over a 5 day period, e.g., at10, 12, 24 and 72 hours after sensor insertion. In certain embodiments,the system may be configured for single point calibration, e.g., asdescribed in U.S. Pat. No. 6,121,009 and elsewhere. In otherembodiments, exemplary calibration schedule may include calibration 1-2times over a 5-7 day period. The system may be configured to acceptcalibration values that fall within a certain range or are at least meeta threshold value. For example, calibration values may be accepted forblood glucose input between about 60 and about 300 mg/dL and when theabsolute rate of change of glucose is estimated to be less than about 2mg/dL/min. These constraints on the acceptance of calibration inputvalues are designed to limit the potential adverse effects of theintrinsic physiological lag between interstitial fluid glucose and bloodglucose.

In the embodiments in which at least one calibration by the user isrequired, the system may be configured so that it does not display(i.e., does not report to the user) real-time glucose values from thecontinuous monitor until the first calibration, e.g., at about ten hoursafter sensor insertion in certain instances. This delay after insertionis designed so that the initial system calibration is performed afterthe sensor has reached a stable equilibrium with the surrounding tissue.

Moreover, in one embodiment, the use of fingerstick calibration inresponse to the Freestyle Navigator® system hypoglycemic alarm mayincrease the overall system accuracy.

An exemplary, analyte sensor and sensing system having the high accuracydescribed herein is the Freestyle Navigator® continuous glucosemonitoring system from Abbott Diabetes Care, Inc., of Alameda, Calif.

Kits

Finally, kits are also provided. Embodiments of the subject kits mayinclude one or more highly accurate sensors as described herein.Embodiments may also include a sensor insertion device and/ortransmitter and/or receiver. Embodiments may also include a drugdelivery device such as an insulin pump.

In certain embodiments, a kit may include a blood glucose meter to beused with the continuous sensing system, e.g., for calibration. Themeter may be a separate component from continuous sensing components (inwhich case a communication link for transferring data from the meter tothe sensing system (such as to the receiver) may be included) or may beintegrated therein, e.g., the receiver may include a blood glucosemeter.

The subject kits may also include written instructions for using asensor. The instructions may be printed on a substrate, such as paper orplastic, etc. As such, the instructions may be present in the kits as apackage insert, in the labeling of the container of the kit orcomponents thereof (i.e., associated with the packaging orsub-packaging) etc. In other embodiments, the instructions are presentas an electronic storage data file present on a suitable computerreadable storage medium, e.g., CD-ROM, diskette, etc. In yet otherembodiments, the actual instructions are not present in the kit, butmeans for obtaining the instructions from a remote source, e.g. via theInternet, are provided. An example of this embodiment is a kit thatincludes a web address where the instructions can be viewed and/or fromwhich the instructions can be downloaded. As with the instructions, thismeans for obtaining the instructions is recorded on a suitablesubstrate.

In many embodiments of the subject kits, the components of the kit arepackaged in a kit containment element to make a single, easily handledunit, where the kit containment element, e.g., box or analogousstructure, may or may not be an airtight container, e.g., to furtherpreserve the one or more sensors and additional reagents (e.g., controlsolutions), if present, until use.

EXPERIMENTAL

The accuracy of a highly accurate continuous monitoring system such asthe Freestyle Navigator® continuous glucose monitoring system measuringglucose in the interstitial fluid is studied, in comparison with alaboratory reference method over five days of sensor wear.

Study Design and Methods

Fifty-eight subjects with Type 1 diabetes ranging in age from 18-64 wereenrolled in a multi-center, prospective, single-arm study. Each subjectwore two sensors simultaneously—one on the arm and the other on theabdomen. All the FreeStyle Navigator® devices were calibrated withseparate capillary fingerstick measurements at 10, 12, 24 and 72 hoursafter sensor insertion. Data from the continuous glucose monitor wascollected at one-minute intervals for the entire study. Measurementsfrom the FreeStyle Navigator® system were compared with reference venoussample measurements taken in an in-patient clinical research center onceevery fifteen minutes over a fifty hour time period covering adistribution over the entire 120 hour wear period for the FreestyleNavigator® sensor.

The subjects were admitted to a healthcare facility either in theevening or in the morning for sensor insertion. The sensors wereinserted by a health care professional on both the lateral or posteriorupper arm and the right or left lower abdominal quadrant using thedisposable sensor delivery unit. The subjects returned to the clinicapproximately nine hours later for the placement of the venous accessline and for the calibration of the sensor using the built-in FreeStyle®blood glucose meter. Calibration of the FreeStyle Navigator® device inthis study was deliberately scheduled to occur at different times of dayas well as both pre- and post-prandially. During two separate periods inwhich the subjects were in the clinic and venous samples were beingtaken, each subject was administered intravenous insulin or a 75 gramfast-acting glucose drink, such as Glucola, in order to obtain data forevaluation of the sensor performance during deliberately-induced periodsof rapidly-falling and rapidly-rising glucose. Data from the sensor andtransmitter were stored in the receiver with a one minute frequency, butwere not displayed to the subjects or the clinic staff. Throughout thestudy, all the subjects continued with their previously establisheddiabetes management regimen. The high frequency and volume of the venousblood samples, 2.5 mL once every fifteen minutes, required a limitationof 50 hours of intensive testing in order to maintain the total volumeof blood drawn from each subject within accepted safety limits. Subjectswere assigned to different study schedules so as to provide an optimaldistribution of the fifty hours of laboratory reference data over thetotal five day duration of the sensor life.

FIG. 2 illustrates five-day data from the Freestyle Navigator®continuous glucose monitor (arm and abdomen) and 50 hours of YSI venoussampling taken two separate in-patient admissions from one subject. Thetiming of the glucose and insulin challenges is also shown. The shadedblocks are night time. The black solid line is the Freestyle Navigator®sensor in the arm, the dashed line is the sensor in the abdomen. YSImeasurements are shown in white triangles. The plus and cross symbolsare the Freestyle Navigator® system blood glucose calibrations for thearm and abdominal sensors, respectively.

Referring to FIG. 2, a typical profile plot for the five-days of thestudy with one-minute data from the arm and abdominal sensors as well asthe fifteen minute venous samples taken over three separate periodsduring the five days. The glucose concentration from the venous samplewas measured using a YSI 2300 STAT Plus™ Glucose & Lactate Analyzer YSIanalyzer (YSI Life Sciences, Yellow Springs, Ohio). All YSI measurementswere made in duplicate from a single blood sample. YSI measurements weremultiplied by 1.12 to obtain plasma equivalent value.

Results

A number of separate metrics were used to evaluate the accuracy andperformance of the FreeStyle Navigator® system compared with the venousblood samples measured with the laboratory reference method. Thesemetrics included the Clarke error grid, the Consensus error grid, themean and median absolute relative difference as well ascross-correlation statistics for comparison of abdominal and upper armsensors. The sensor performance was evaluated for the entire five days,for each day individually as well as diurnally and nocturnally.Characteristic physiological lag times were derived from analysis of thedata. The data was also analyzed using the Continuous Glucose Error GridAnalysis (CG-EGA) (Kovatchev, 2004). Finally, the accuracy of theFreeStyle Navigator® system compared to the venous reference samples wasanalyzed as a function of the measured rates of change in the underlyingblood glucose.

Comparison of the FreeStyle Navigator® continuous glucose measurementswith the laboratory reference method (n=20,362) gave a mean absoluterelative difference of 12.8% and a median absolute relative differenceof 9.3%. The percentage in the clinically-accurate Clarke Error Gridzone A was 81.7% and 16.7% in the clinically-acceptable B zone. Thisincluded periods of high rates of change of blood glucose duringintravenous glucose and insulin challenges. The precision of the matchedFreestyle Navigator® sensors worn on the arm and abdomen had acoefficient of variation of 10% (n=312,953). The accuracy remainedunchanged over the five days with the percent of data in the ClarkeError Grid Zone A equal to 82.5% on the first day and 80.9% on the fifthday.

Clinical Accuracy Overall

FIG. 3 shows the Clarke error grid for the study reported herein. Morespecifically, FIG. 3 illustrates an overall Clarke error grid showing81.7% in the clinically-accurate A zone, 16.7% of the paired points inthe clinically-acceptable or benign error zone B and only 1.7% outsideof the A and B zones

The Clarke error grid was developed to assess the clinical implicationsof new glucose monitoring technology relative to accepted referencemethods (Cox D J, Clarke W L, Gonder-Frederick L A, Pohl S, Hoover C,Snyder A, “Accuracy of perceiving blood glucose in IDDM”, Diabetes Care,8(6):529-36, 1985; Clarke W L, Cox D, Gonder-Frederick L A, Carter W andPohl S L. “Evaluating clinical accuracy of systems for self-monitoringof blood glucose” Diabetes Care, 10, 622-628, 1987). There were a totalof 20,362 paired points for all 58 subjects with YSI venous measurementsand Freestyle Navigator® system interstitial fluid glucose measurements.81.7% of the paired points fell in the Clarke error grid zone Aindicating a high level of clinical accuracy. There were 16.7% of thepaired points in the clinically-acceptable (benign error) zone “B”, 0.1%in the overtreatment error zone “C”, 1.9% in the failure to detect errorzone “D” and 0.01% in the clinically inaccurate and dangerous error zone“E”.

The Consensus error grid has been proposed as an alternative to theoriginal error grid zone demarcations, specifically to eliminate thephysical proximity of the clinically-unacceptable D zone with theclinically-accurate A zone in the lower left portion of the grid. Theresults of the Clarke error grid and the Consensus error grid aresummarized in the Table (1) below. The Consensus error grid was alsodefined with five distinct risk levels, but the definitions werespecified in terms of effect on clinical action by the patient. Zone Ahas no effect. Zone B has little or no effect. Zone C has alteredclinical action. Zone D has altered clinical action with significantmedical risk. Zone E has altered clinical action with potentiallydangerous consequences.

On the Clarke error grid, there were 316 individual points in the Dzone. Ninety-five percent of these points were in the lower leftquadrant of the error grid. TABLE 1 Summary statistics of Clarke andConsensus Error Grid Clarke Consensus Error Error Grid Grid Zone % N =20362 % N = 20362 A 81.7 16627 85.5 17419 B 16.7 3398 13.6 2776 C 0.1 190.8 161 D 1.6 316 0.03 6 E 0.0 2 0.0 0

On the Consensus error grid, by contrast, the number of points in thesignificant medical risk D zone is reduced to 6. In addition to thereduction in D zone points, the Consensus error grid shows a higherpercentage in the clinically-accurate A zone, a slightly lowerpercentage in the clinically-acceptable B zone, a slightly higherpercentage in the altered clinical action C zone and no points in thedangerous consequence E zone.

The performance of the Freestyle Navigator® system was also assessedusing the mean and median absolute relative difference between thesensor interstitial glucose measurements and the YSI venous samplemeasurements. The mean absolute relative difference was 12.8% and themedian absolute relative difference was 9.3%. A comparison of accuracyand performance by day shows that the system's performance on the fifthday is equivalent to the performance of the first or second day. Table(2) contains data with the error grid statistics as well as the mean andmedian absolute relative difference from the study separated by day.TABLE 2 Clarke Error Grid, mean and median absolute relative differenceby day Day 1 Day 2 Day 3 Day 4 Day 5 Zone N % N % N % N % N % A 435482.5 3215 82.4 2903 79.4 1688 84.0 4467 80.9 B 865 16.4 646 16.6 66818.3 285 14.2 934 16.9 C 12 0.2 4 0.1 1 0.0 0 0.0 2 0.0 D 47 0.9 34 0.982 2.2 37 1.8 116 2.1 E 0 0.0 2 0.1 0 0.0 0 0.0 0 0.0 Mean ARD 12.6 12.314.1 11.9 13.0 Median ARD 9.4 9.3 9.9 7.8 9.5 Total 5278 100.0 3901100.0 3654 100.0 2010 100.0 5519 100.0

Additional analysis was done comparing the accuracy and performance ofthe Freestyle Navigator® system nocturnally and diurnally. Thepercentage of points in the Clarke error grid A zone was 87.1% at nightand 80.6% during the day. The difference in accuracy during the day maybe associated with the higher rates of change during the daytime, whenall of the glucose and insulin challenges were conducted.

The data from the present study has also been analyzed using theContinuous Glucose Error Grid Analysis (CG-EGA), designed to incorporatethe extra temporal dimension of data provided by continuous glucosemonitoring systems (Kovatchev et al.). The rate analysis using theCG-EGA gave a 81.1% in the rate error grid A zone, 14.4% in the rateerror grid B zone, 1.5% in the rate error grid C zone, 2.3% in the rateerror grid D zone, and 0.7% in the rate error grid E zone. The pointanalysis using the CG-EGA gave a 83.6% in point error grid A zone, 15.0%in point error grid B zone, 0.1% in point error grid C zone, 1.3% inpoint error grid D zone, and 0% in point error grid E zone. The CG-EGAanalysis combining rate and point information revealed that accuracy,measured as a percentage of accurate readings plus benign errors, was97.5% (94.2% accurate, 3.4% benign). The CG-EGA accuracy stratified byglycemic state gave 60.4% in hypoglycemia (53.1% accurate, 7.3% benign),99.3% in euglycemia (95.7% accurate, 3.6% benign) and 98.2% inhyperglycemia (95.4% accurate, 2.8% benign). The difference in accuracybetween the hypoglycemic, euglycemic, and hyperglycemic ranges may berelated to the high rate of change often associated with the descentinto hypoglycemia. Standard egression analysis and Deming regressionanalysis both gave small, but significant offsets 24.9 and 14.3 mg/dL)that could contribute to the slight decrease in accuracy inhypoglycemia.

FIGS. 4A and 4B give an expanded view of the data from FIG. 2 on afour-hour time axis and centered about the glucose challenge and theinsulin challenge, respectively. More specifically, FIG. 4A illustratesa zoomed in view (four hour duration) of Freestyle Navigator®™ sensordata and YSI measurements during the glucose challenge. Referring toFIG. 4A, the continuous glucose sensor data in one minute intervals areshown in the two solid curves (solid from the arm, dashed from theabdomen). The 15 minute YSI venous sample data are shown in thetriangles. The time between the nadir of the YSI data and the FreestyleNavigator® system is approximately 24 minutes. The time between the peakof the YSI data and the Freestyle Navigator® system data isapproximately 19 minutes.

Additionally, FIG. 4B shows data from two Freestyle Navigator® sensors,compared with fifteen minutes venous samples measured with the YSI fromthe insulin challenge in one patient in the study. Referring to FIG. 4B,the Freestyle Navigator® projected alarm, would have alerted the subjectto an impending hypoglycemic event 26 minutes before the blood sugarcrossed the 70 mg/dL hypoglycemic threshold. At the time of the alarm,the Freestyle Navigator® system glucose was approximately 175 mg/dL andthe YSI reading was approximately 90 mg/dL and the rate of change was−3.5 mg/dL/min.

Both FIGS. 4A and 4B show the temporal tracking of the FreeStyleNavigator® system compared against the venous reference samples. Theexpanded temporal axis used in FIGS. 4A and 4B also permits more directvisualization of the time lag between the Freestyle Navigator® systeminterstitial fluid glucose measurement and the venous reference samplemeasurements. The temporal offset between the FreeStyle® Navigatorsystem and the venous reference measurements was also analyzed byapplying a time shift in order to minimize the mean absolute relativedifference.

After correction for the calibration bias, this resulted in an average12.8 minute lag between the glucose values measured in the interstitialfluid and in the venous samples. This is consistent with previouslypublished studies on the physiological lag between interstitial fluidglucose and blood glucose (see for example: Rebrin K, Steil G M, vanAntwerp W P, Mastrotoraro J J, “Subcutaneous glucose predicts plasmaglucose independent of insulin: implications for continuous monitoring”,Am J Physiol., 277(3 Pt 1):E561-71, 1999; Steil G M, Rebrin K,Mastrototaro J, Bernaba B, Saad M F, “Determination of plasma glucoseduring rapid glucose excursions with a subcutaneous glucose sensor”,Diab. Tech. Ther, 5:27-31, 2003; Thennadil S N, Rennert J L, Wenzel B J,Hazen K H, Ruchti T L, Block M B, “Comparison of glucose concentrationin interstitial fluid, and capillary and venous blood during rapidchanges in blood glucose levels”, Diab. Tech. Ther., 3(3):357-65, 2001).

The performance of the arm and abdominal sensors was comparable withequivalent Clarke error grid statistics and mean absolute relativedifference. The precision of the matched Freestyle Navigator® sensorsworn on the arm and abdomen had a coefficient of variation of 10%(n=312, 953). There was no difference in performance of the sensor as afunction of age, gender or ethnicity. However, there were small butmeasurable differences in the accuracy of the sensor depending on thesubject's BMI and also on the years since diagnosis. Subjects with BMIless than 25.0 had 78.8% in the Clarke error grid A zone (N=4844),whereas subjects with BMI between 25.0 and 30.0 had 82.2% in the Clarkeerror grid A zone (N=7855) and subjects with BMI greater than 30.0 had84.4% in the Clarke error grid A zone (N=3928). Similarly, there weresmall but measurable differences in accuracy depending on the yearssince diagnosis of type 1 diabetes. The highest accuracy, 88.5% in theClarke error grid A zone, was found in subjects who had been diagnosedwith diabetes for five years or less (N=2066) and 81.3% for subjectsdiagnosed between 5 and 25 years (N=9133). Subjects diagnosed with type1 diabetes for over 25 years had 79.9% in the Clarke error grid A zone(N=5448).

Clinical Accuracy Under Special Circumstances

The evaluation of the overall accuracy and performance of the FreeStyleNavigator® continuous glucose monitor included periods ofdeliberately-induced rapidly rising and rapidly falling blood glucose,i.e. in response to the glucose and insulin challenges. There weresignificant differences in the accuracy compared with the laboratoryreference measurements depending on the different rates of change of theunderlying blood glucose. Table (3) gives the Clarke error gridstatistics and the median absolute relative difference percentage as afunction of the rate of change of blood glucose as determined by the YSImeasurements. The effect of the physiological lag on the accuracy of thesensor values compared to venous reference samples is more pronounced atthe high rates of change, particularly during when the absolute rate ofchange exceeds 2 mg/dL/min. TABLE 3 Rate of change and Clarke error gridstatistics and median ARD Rate of Change Clarke Error Grid Region Median(mg/dL/min) N A B C D E ARD % <−2  601 54.6 42.3 1.3 1.8 0.0 17.4 −2 to−1 1728 71.7 26.2 0.3 1.8 0.0 11.8 −1 to 1  14653 84.9 13.5 0.0 1.5 0.08.5 1 to 2 1954 79.8 18.9 0.0 1.3 0.0 11.0 >2 691 63.5 34.7 0.0 1.7 0.016.9

FIG. 5 illustrates the rate of change histogram showing underlying rateof change at high resolution (in units of 0.25 mg/dL/min) and in unitsof the Navigator receiver trend arrows (1.0 mg/dL/min). The rate ofchange of glucose as measured by the sensor was between −1 and +1mg/dL/min 74.6% of the time. Referring to FIG. 5, there is a slightdifference in the measured occurrence of absolute rates of change lessthan 1 mg/dL/min due to the different sampling frequency and temporalextent of the Freestyle Navigator® system and YSI measurements.

The Freestyle Navigator® trend arrows would have been in the horizontalposition indicating an absolute rate of change less than 1 mg/dL/min74.1% of the time for which the YSI data revealed 71.9% of all readingsin this range. Both values are consistent with previously reportedresults (see for example: Dunn T C, Eastman R C, Tamada J A, “Rates ofglucose change measured by blood glucose meter and the GlucoWatchBiographer during day, night, and around mealtimes”, Diabetes Care 27:2161-2165, 2004; Kovatchev, B. P., Clarke, W. L., Breton, M., Brayman,K. and McCall, A. “Quantifying Temporal Glucose Variability in Diabetesvia Continuous Glucose Monitoring: Mathematical Methods and ClinicalApplication” Diab. Technol. Thera., 7, 849-862, 2005).

FIG. 6 illustrates Clarke error grid for YSI rates of change between −1to 1 mg/dL/min showing increase in accuracy during modest rates ofchange. Referring to FIG. 6, whereas the overall percentage of pairedpoints in the Clarke error grid A zone was 81.7%, the percentage in theA zone for rates of change between −1 mg/dL/min and +1 mg/dL/min wassignificantly higher at 84.9%. Similarly, the mean and median absoluterelative differences at these times were 11.4% and 8.5% respectively.

The accuracy of the Freestyle Navigator® continuous glucose monitor wasevaluated in comparison to a standard laboratory reference method usingvenous blood samples. The overall mean and median absolute relativedifference of the sensor in the current study of 12.8% and 9.3%represent a significantly higher level of accuracy than previouslypublished results from other continuous glucose monitoring systems (seefor example, Diabetes Research in Children Network (DirecNet) StudyGroup: “The Accuracy of the CGMS in Children with Type 1 Diabetes:Results of the Diabetes Research in Children Network (DirecNet) AccuracyStudy”. Diabetes Technol Ther 5(5):781-789, 2003; Diabetes Research inChildren Network (DirecNet) Study Group: “The Accuracy of the GlucoWatchG2 Biographer in Children with Type 1 Diabetes: Results of the DiabetesResearch in Children Network (DirecNet) Accuracy Study”. DiabetesTechnol Ther 5(5):791-800, 2003; Tansey M J, Beck R W, Buckingham B A,Mauras N, Fiallo-Scharer R, Xing D, Kollman C, Tamborlane W V, Ruedy KJ, “Accuracy of the modified Continuous Glucose Monitoring System (CGMS)sensor in an outpatient setting: results from a diabetes research inchildren network (DirecNet) study.” Diab. Tech. Ther. 7(1):109-14, 2005;Garg S., Zisser H., Schwartz S., Bailey T., Kaplan R., Ellis S. andJovanovic L, “Improvement in glycemic excursions with a transcutaneous,real-time continuous glucose sensor”, Diabetes Care, 29, 44-50, 2006).

The high accuracy of the system as measured by the percentage in theClarke error grid A zone and the mean and median absolute relativedifferences remained high over the entire five days. There was a small,but measurable improvement in the Clarke error grid statistics and theabsolute relative difference values on the fourth day. This is dueprincipally to the fact that there were no glucose challengesadministered on the fourth day of the study resulting in fewer rates ofchange on that day less than 2 mg/dL/min than on other days. Inaddition, there may be a small increase in accuracy on the fourth dayassociated with the final system calibration at 72 hours after sensorinsertion. Similarly, the slight decrease in accuracy observed on thethird and fifth days of the sensor wear may be associated with the factthat these days had a greater number of glucose and insulin challengesthan other days in the study, resulting in more absolute rates of changeon those days in excess of 2 mg/dL/min.

A significant portion of the apparent discrepant points between theFreestyle Navigator® and the venous reference samples are likely due tothe physiological lag alone. An example of the effect of physiologicallag on accuracy is the point at the nadir of the curves in FIG. 4B,which is categorized in the Clarke error grid analysis as a clinicallyunacceptable D zone point. In this case, although the point-wisecomparison of the Freestyle Navigator® sensor value and the venousreference sample value suggests a failure to detect a hypoglycemicevent, it is clear from the data that the Freestyle Navigator® system iscorrectly tracking the fall of the subject's glucose level.

In the case shown in FIG. 4B, with the projected alarm capabilityenabled and the detection threshold set at 70 mg/dL, the device wouldhave alerted the user to a predicted change in clinical state fromeuglycemia to hypoglycemia when the Freestyle Navigator® glucose valuewas approximately 175 mg/dL and the measured rate of glucose decreasewas in excess of −3.5 mg/dL/min. At that moment, the trend arrow was inthe downward vertical direction, indicating a rate of glucose decreaseof greater than 2 mg/dL/min, and the device's alarm would have usedpredictive algorithm to identify that the subject would be hypoglycemicin thirty minutes.

At the time when the projected alarm would have alerted the subject toan impending hypoglycemic event, the YSI reading was approximately 90mg/dL. An interpolation of the YSI data indicates that the subject'sblood sugar crossed the 70 mg/dL threshold for hypoglycemiaapproximately twenty-six minutes later. Although the paired YSI andFreestyle Navigator® system points at the nadir of the curve result in aD zone point on the Clarke error grid, it is clear from a detailedanalysis that the projected alarm would have alerted the subject to animpending hypoglycemic event in a timely manner.

Another important measure of the clinical accuracy, and ultimately theclinical utility, of the Freestyle Navigator® system is the percentageof points in the clinically-accurate Clarke error grid A zone. A recentnumerical simulation study evaluated the effect of sensor inaccuracy onthe statistics associated with glucose monitoring error grid analysisusing data from a clinical trial of a continuous glucose monitoringsystem in type 1 children and adolescents (Kollman et al., 2005). In thenumerical study, paired points from the actual continuous glucosemonitoring system and a laboratory reference method were randomly“shuffled” to simulate a high degree of sensor inaccuracy. The studyfound that 78% of the randomly shuffled paired points were still in thecombined A and B zones of the Clarke error grid. A more useful measureof the clinical accuracy and utility of new glucose monitoringtechnology may be the percentage of points in the clinically-accurateClarke error grid A zone alone. (Kollman C, Wilson D M, Wysocki T,Tamborlane W V, Beck R W, “Limitations of the Statistical measures ofError in Assessing the Accuracy of continuous Glucose Sensors”, Diab.Tech. Ther., 7(5):665-672, 2005). An alternative to the morecommonly-used metric of combined A and B zone percentage is to relyinstead on the total percentage in the A zone alone. The results of thepresent study showing the Freestyle Navigator® system achieving 81.7% inthe A zone alone represent a new level of performance for continuousglucose monitoring systems.

The high accuracy and performance of the Freestyle Navigator® system atnight is also in contrast with previous reports of continuous glucosemonitoring systems that exhibited sustained periods of anomalousnocturnal hypoglycemia (see for example: McGowan K, Thomas W, Moran A.“Spurious reporting of nocturnal hypoglycemia by CGMS in patients withtightly controlled type I diabetes” Diabetes Care 2002; 25: 1499-1503;Metzger M, Leibowitz G, Wainstein J, Glaser B, Raz I. “Reproducibilityof glucose measurements using the glucose sensor” Diabetes Care 2002;1185-1191; Mauras N, Beck R W, Ruedy K J, Kollman C, Tamborlane W V,Chase H P “Lack of accuracy of continuous glucose sensors in healthy,nondiabetic children: results of the Diabetes Research in (ChildrenNetwork (DirecNet) accuracy study” J Pediatr 2004; 144:770-775).

The difference in accuracy as a function of BMI may be related to thelength of the Freestyle Navigator® sensor and the thickness of thesubcutaneous adipose tissue layer in subjects with BMI less than 25.Anthropometric data strongly suggests that the insertion of theFreestyle Navigator® sensor in the upper arm or abdomen will result inthe sensor being placed as intended in the subcutaneous adipose tissuelayer in most individuals (Horejsi, R., Moller, R., Pieber, T R,Wallner, S., Sudi, K, Reibnegger, G. and Tafeit “Differences ofsubcutaneous adipose tissue topography between type 2 diabetic men andhealthy controls” Exp. Biol. Med., 227, 794-798, 2002). However, in someindividuals with low BMI, the data indicate that the subcutaneousadipose tissue layer thickness on the posterior arm upper arm or eventhe lower abdominal quadrant may be only slightly greater than therequired 6 mm thickness needed to properly accommodate the sensor.Although the overall sensor performance in subjects with BMI less than25 is still excellent (78.8% in the clinically-accurate Clarke errorgrid A zone), there is a small but measurable difference when comparedwith subjects with BMI greater than 30 (84.4% in the clinically-accurateClarke error grid A zone). In the low BMI subjects with reducedsubcutaneous adipose tissue layer thickness, the proximity of skeletalmuscle tissue to the sensor in the adipose tissue could increase theeffect reported by Moberg et al. in which tissue glucose nadirs were notonly delayed relative to plasma, but also reduced especially duringinsulin-induced hypoglycemia (Moberg E, Hagstrom-Toft E, Arner P. andBolinder J. “Protracted glucose fall in subcutaneous adipose tissue andskeletal muscle compared with blood during insulin-inducedhypoglycaemia” Diabetologia 40, 1320-1326, 1997).

In the present study, the apparent difference in accuracy as a functionof years since diagnosis is most likely also a result of the weakdependence of accuracy on BMI. The 6 subjects with a diagnosis ofdiabetes less than five years, for whom there was the highest percentagein the Clarke error grid A zone and the lowest median absolute relativedifference, also by chance had the highest mean BMI (29.8). Similarly,the 18 subjects with lowest BMI (<24.9) in the study happened to alsohave the highest mean years since diagnosis of diabetes (30.1 years).

Insulin Adjustment Procedure Clinical Decision Analyses

Insulin Adjustment Analysis

The Insulin Adjustment Analysis evaluates the difference between insulindosing based on Freestyle Navigator®Continuous Glucose Monitoring System(CM) readings and that based on reference readings. The interpretationof the analysis is best understood considering a hypothetical patientwith a glucose target level of 90-120 mg/dL and an insulin sensitivityof 30 mg/dL/unit. The glucose target level represents aggressive therapywhere the therapeutic goal is to keep glucose squarely in the normalrange. The analysis is targeted to meet the requirements of intensiveinsulin therapy. The choice of insulin sensitivity was made to simplifyinterpretation—the treatment differences between Navigator CM and YSIare calculated in whole number differences in the units of insulin. Thisseemingly arbitrary choice of the hypothetical patient has no influenceon the results of the Insulin Adjustment Analysis—the choice was basedon the goals of intensive insulin therapy and the ease of interpretationof the results.

The Insulin Adjustment Analysis data is reported as differences in unitsof insulin. (see Table 4). This is an intermediate result that allows amore detailed characterization of the data than the final summary (seeTable 5). Decisions with Navigator CM were rated Correct 89.3% (1180/1322) of the time and Acceptable 7.6% ( 100/1322) of the time.Since the Acceptable rating translates to a glucose adjustment to withinthe normal glucose range, accurate adjustments are the sum of Correctand Acceptable categories, 96.8% ( 1280/1322). TABLE 4 TreatmentDifference for the Hypothetical Patient with Insulin Sensitivity = 30mg/dL/unit and Glucose Target = 90−120 mg/dL Navigator CM YSI TreatmentDifference (Units of Glucose <200 mg/dL Glucose ≧200 mg/dL insulin) N %Category N % Category −4 0 0 Hypergly- 4 0.6 Hypergly- cemia 2 cemia 2−3 1 0.1 Hypergly- 13 2.0 Hypergly- cemia 1 cemia 1 −2 11 1.6 Acceptable78 12.1 Acceptable −1 120 17.7 Correct 215 33.4 Correct 0 353 52.0Correct 240 37.3 Correct 1 173 25.5 Correct 79 12.3 Correct 2 18 2.7Possible 11 1.7 Acceptable Error 3 2 0.3 Error 3 0.5 Possible Error 4 10.1 Error 0 0 Error Total 679 100 643 100

TABLE 5 Insulin Adjustment Analysis Summary Category Effect on BloodGlucose N % Correct Within ±30 mg/dL of target glucose 1180 89.3Acceptable Within normal glucose range 100 7.6 Possible Error 60 mg/dLbelow target glucose 21 1.6 (hypo) Error (hypo) ≧90 mg/dl below targetglucose 3 0.2 Hyperglycemia 1 90 mg/dL above target glucose 14 1.1Hyperglycemia 2 ≧120 mg/dl above target glucose 4 0.3 Total 1322 100

In summary, this analysis describes 3 occurrences of “Error (hypo)” and4 occurrences of “hyperglycemia 2” being potentially indicated from 1322decision points analyzed.

Glucose Peak

Continuous glucose monitoring provides the ability to identify andquantify the maximum glucose excursions after meals and during thenight. The quantification of glucose peaks was clinically accurate 88.1%of the time and clinically useful 97.6% of the time (see Table 6). TABLE6 Glucose Peak Analysis Difference Clinical Assessment N % ±15 mg/dLAccurate 263 41.5 ±45 mg/dL Accurate 295 46.6 ±75 mg/dL Useful 60 9.5±105 mg/dL Misclassification 14 2.2 ±135 mg/dL Misclassification 1 0.2Total 633 100.0Insulin Adjustment Analysis

The Insulin Adjustment Analysis evaluates the hypothetical differencebetween insulin dosing based on Navigator CM readings to that based on ablood glucose meter such as Freestyle Blood Glucose (BG) readings. Theinterpretation of the analysis is best understood considering ahypothetical patient with a glucose target level of 90-120 mg/dL and aninsulin sensitivity of 30 mg/dL/unit. The glucose target levelrepresents aggressive therapy where the therapeutic goal is to keepglucose squarely in the normal range. The analysis is targeted to meetthe requirements of intensive insulin therapy. The choice of insulinsensitivity was made to simplify interpretation—the treatmentdifferences between Navigator CM and Freestyle BG YSI (see Table 7) arecalculated in whole number differences in the units of insulin. Thisseemingly arbitrary choice of the hypothetical patient has no influenceon the results of the Insulin Adjustment Analysis—the choice was basedthe goals of intensive insulin therapy and the ease of interpretation ofthe results.

The Insulin Adjustment Analysis data is reported as differences in unitsof insulin (see Table 7). There were 6,040 paired (NavigatorCM-Freestyle BG) glucose readings available at times of subject-reportedinsulin dosing or bedtime in the Home Use Study. The analysis issummarized in Table 8 with 86.5% ( 5226/6040) of the readings correctand 94.3% ( 5696/6040) accurate or acceptable. These results provideapproximately 89.3% ( 1180/1322) correct and 96.8% ( 1280/1322) accurateor acceptable. TABLE 7 Treatment Difference for the Hypothetical Patientwith Insulin Sensitivity = 30 mg/dL/unit and Glucose Target = 90-120mg/dL Difference in Insulin Glucose <200 mg/dL Glucose ≧200 mg/dL Dose(Units) N (%) N (%) 4 0 0 1 0.0 3 11 0.3 2 0.1 2 84 2.1 14 0.7 1 81020.1 162 8.1 0 2362 58.5 530 26.5 −1 675 16.7 687 34.3 −2 89 2.2 36718.3 −3 8 0.2 163 8.1 −4 0 0 75 3.7 Total 4039 — 2001 —

TABLE 8 Insulin Adjustment Analysis Summary Category Effect on BloodGlucose N % Correct Within ±30 mg/dL of target glucose 5226 86.5Acceptable Within normal glucose range 470 7.8 Possible Error 60 mg/dLbelow target glucose 86 1.4 (hypo) Error (hypo) ≧90 mg/dl below targetglucose 12 0.2 Hyperglycemia 1 90 mg/dL above target glucose 171 2.8Hyperglycemia 2 ≧120 mg/dl above target glucose 75 1.2 Total 6040 100

Insulin dosing or bedtime was not indicated for 5,447 of the 11,487Freestyle BG duplicate points. The Insulin Adjustment Analysis was alsoconducted using the 5,447 Freestyle BG duplicate points for which therewas no indication of insulin injection to determine if there was asubstantive difference between the two populations. The InsulinAdjustment Analysis data is reported as differences in units of insulin(See Table 9). The results are slightly better for the points whereinsulin injections were not indicated (See Table 10) with 89.4% (868/5447) correct and 95.5 ( 5203/5447) correct or acceptable. TABLE 9Treatment Difference for the Hypothetical Patient with InsulinSensitivity = 30 mg/dL/unit and Glucose Target = 90-120 mg/dL -Non-insulin Injection Points Difference in Insulin Glucose <200 mg/dLGlucose ≧200 mg/dL Dose (Units) N (%) N (%) 4 2 0.0 0 0 3 11 0.3 1 0.1 295 2.3 26 2.0 1 876 21.2 132 10.0 0 2473 59.9 388 29.5 −1 588 14.2 41131.2 −2 81 2.0 228 17.3 −3 5 0.1 88 6.7 −4 0 0 42 3.2 Total 4131 — 1316—

TABLE 10 Insulin Adjustment Analysis Summary Non-insulin InjectionPoints Category Effect on Blood Glucose N % Correct Within ±30 mg/dL oftarget glucose 4868 89.4 Acceptable Within normal glucose range 335 6.2Possible Error 60 mg/dL below target glucose 96 1.8 (hypo) Error (hypo)≧90 mg/dl below target glucose 13 0.2 Hyperglycemia 1 90 mg/dL abovetarget glucose 93 1.7 Hyperglycemia 2 ≧120 mg/dl above target glucose 420.8 Total 5447 100

When a patient adjusts an insulin dose using a blood glucose meter suchas Freestyle Blood Glucose monitor, there is no indication if glucose ischanging. If glucose is rising at the time of glucose dosing, there isinsufficient insulin to stabilize blood glucose and the predictedinsulin dose will be too small. Likewise, if glucose is descending,there is already insulin in the blood, and the predicted insulin dosewill be too large. The rate of glucose change indicated by Navigator CMat the time of insulin dosing (see Table 11) indicates glucose changes≧±2 mg/dL/minute 4.0% of the time, and ≧±1 mg/dL/minute 18.3% of thetime. The agreement of static the blood glucose meter readings withstatic reference readings is excellent, but the interpretation of thisagreement to suggest accurate insulin dosing with the blood glucosemeter is not correct. When insulin is dosed with no knowledge ofchanging glucose levels, the dosing will be incorrect a significantfraction of the time. The determination of 94.3% Navigator CM dosingaccuracy in this study and 96.8% Navigator CM dosing accuracy in aprevious study provide realistic estimations when the rate of glucosechange is also known. TABLE 11 Navigator CM Rate Indication at the Timeof Insulin Dosing Navigator CM Rate of Change (mg/dL/minute) N (%) >2.0330 3.3 1.0 to 2.0 897 9.0 −1.0 to 1.0  8140 81.7 −2.0 to −1.0 526 5.3<−2.0  72 0.7

The description below details a further user study results from a highlyaccurate continuous glucose monitoring system such as, for example,Freestyle Navigator® system. Of the 137 subjects enrolled in theinvestigation, 123 completed the 40-day monitoring period. The other 14subjects withdrew from the study due to non-compliance with protocoldemands (n=8) or difficulties handling the device (n=6). None of thediscontinued subjects participated in the unblinded portion of thestudy. The glucose data available for the discontinued subjects wasincluded in the paired point analysis.

The performance of the Freestyle Navigator® was assessed using theabsolute relative difference between the sensor interstitial glucosemeasurements and the blood glucose measurements. Data from 961 sensorswith 11,487 paired FreeStyle BG reference values were evaluated. Themean absolute relative difference was 14.4% and the median absoluterelative difference was 11.1%. The mean absolute relative differenceindicates that, on average, the CM reading was 14.4% higher or lowerthan the corresponding BG reading. The median absolute relativedifference indicates that the CM reading was equally as likely to bewithin 11.1% of the BG reading, either higher or lower, as it was to beoutside of that range.

The equation for the Deming regression had a slope of 0.83, an interceptof 21.8 mg/dL and correlation coefficient of 0.92. These resultsdemonstrate a strong correlation between CM and BG readings.

FIG. 7 shows the Clarke error grid for the study. There were a total of11,487 paired points with averaged duplicate BG reference values andinterpolated CM values, from 131 subjects. No paired points wereavailable from six subjects. Of the 11,487 paired points, 77.2% fell inthe Clarke error grid zone A, indicating a high level of correspondencebetween the reference blood glucose measurements and the CM results.There were 19.6% of the paired points in zone B and only 3.2% outsidethe A and B zones. Results for all the Clarke error grid zones are shownin Table 12 below. The results of the Consensus error grid are alsoincluded in Table 12. TABLE 12 Summary statistics of Clarke andConsensus Error Grid Clarke Error Consensus Grid Error Grid Zone N (%) N(%) A 8863 77.2 9180 79.9 B 2255 19.6 2194 19.1 C 1 0.0 109 0.9 D 3653.2 4 0.0 E 3 0.0 0 0.0 N paired points 11487 11487

On the Clarke error grid, there were 365 individual points in the Dzone. On the Consensus error grid, by contrast, the number of points inthe D zone is reduced to four. In addition, the Consensus error gridshows 79.9% in the A zone, 99.0% in the A and B zones, less than 1% inthe C and D zones and no points in the E zone.

A comparison of accuracy and performance by day of sensor wear showsthat the system's performance on the fifth day is nearly equivalent tothe performance on the first or second day. Table 13 contains data withthe error grid statistics as well as the mean absolute relativedifference from the study separated by day. TABLE 13 Clarke Error Grid,absolute relative difference by day Day 1 Day 2 Day 3 Day 4 Day 5 ZoneN/(%) N/(%) N/(%) N/(%) N/(%) Clarke A 1061 2182 2110 1884 1626 (77.8)(77.4) (77.7) (79.3) (73.5) Clarke B 266 551 516 427 495 (19.5) (19.6)(19.0) (18.0) (22.4) Clarke C 0 0 0 0 1 (0.0) (0.0) (0.0) (0.0) (0.0)Clarke D 36 84 91 63 91 (2.6) (3.0) (3.3) (2.7) (4.1) Clarke E 1 1 0 1 0(0.1) (0.0) (0.0) (0.0) (0.0) N paired 1364 2818 2717 2375 2213 pointsConsensus A Consensus B Consensus C Consensus D Consensus E N paired1364 2818 2717 2375 2213 points Mean 14.8 14.3 14.0 13.9 15.3 ARD MedianARD

Table 14 shows that the CM readings are optimal when blood glucose isrelatively stable (i.e., when the rate is within +/−1 mg/dL/min). Asexpected the bias increases somewhat as the magnitude of the rate ofglucose change increases. However, the displayed rate arrow provides thenecessary information to properly interpret the glucose result in thesesituations. The mean bias for glucose <100 mg/dL and the mean percentbias for glucose ≧100 mg/dL become increasingly positive as the ratedecreases from +2 mg/dL/minute to −2 mg/dL/minute. Lag in theinterstitial readings versus capillary blood glucose readings is theexplanation for this result. When glucose levels were rising, the CMvalues were low, on average, versus BG with the difference versus BGlower for rising glucose (>1 mg/dL/minute) than for stable glucose (±1mg/dL/minute). When glucose levels were falling CM was high, on average,versus BG with the difference versus BG higher for falling glucose (<1mg/dL/minute) than for stable glucose (±1 mg/dL/minute). TABLE 14Difference measures vs. glucose rate of change Navigator CM Rate ofChange (mg/dL per minute) Mean Median N Difference (mg/dL) for glucose<100 mg/dL >2.0 3.7 −1.2 3 1.0 to 2.0 4.7 5.5 33 −1.0 to 1.0  7.6 7.12028 −2.0 to −1.0 17.9 18.7 261 <−2.0  26.5 24.4 50 Absolute difference(mg/dL) for glucose <100 mg/dL >2.0 11.0 9.8 3 1.0 to 2.0 12.5 9.9 33−1.0 to 1.0  13.3 10.8 2028 −2.0 to −1.0 21.5 19.4 261 <−2.0  32.4 27.050 Percent difference % for glucose ≧=100 mg/dL >2.0 −13.7 −14.3 152 1.0to 2.0 −10.9 −10.7 581 −1.0 to 1.0  −3.5 −3.7 7245 −2.0 to −1.0 6.9 6.8432 <−2.0 7.5 9.1 69 Absolute % difference % for glucose ≧=100mg/dL >2.0 17.0 16.1 152 1.0 to 2.0 14.8 12.6 581 −1.0 to 1.0  12.2 9.87274 −2.0 to −1.0 15.9 12.6 432 <−2.0  18.3 14.5 69

The Clarke EGA as a function of Navigator rate (Table 15) exhibits theexpected behavior. When glucose is descending by at least −2 mg/dL/min,there is a higher likelihood that a reading would fall into the leftZone D than when the glucose is stable or rising. When glucose isrising, there is a higher likelihood that a reading would fall into theright Zone D. The rate arrow provides the valuable information toproperly interpret the glucose result (i.e. when glucose is rapidlydescending Navigator CM tends to be higher than Navigator BG and whenglucose is rapidly ascending Navigator CM tends to be lower thanNavigator BG). TABLE 15 Clarke EGA vs. glucose rate of change Zone <−2.0% −2.0 to −1.0 % −1.0 to 1.0 % 1.0 to 2.0 % >2.0 % A 61 51.3 425 61.37372 79.3 455 74.1 101 65.2 B 45 37.8 194 28.0 1688 18.1 149 24.3 4831.0 C 0 0.0 1 0.1 0 0.0 0 0.0 0 0.0 D 12 10.1 73 10.5 240 2.6 10 1.6 63.9 E 1 0.8 0 0.0 2 0.0 0 0.0 0 0.0 Total 119 692 9302 614 155Sensor Success Measures

The rate of successful sensor insertions was evaluated from reportedresults of each sensor insertion attempt, as well as the electronicrecords stored by the Receiver. The electronic records were used todetermine whether each sensor was detected by the Receiver, and whetherthe user followed the steps in the labeling. The percentage ofinsertions that were successful, when used as directed, was similar forthe blinded (96.0%) and unblinded (96.8%) phases of the study (96.4%overall). The percentage of successful insertions was similar for thearm (95.7%) and abdomen (97.4%) insertion sites. Abdomen insertions mayhave been more successful because it is easier to see the entireinsertion process at the abdomen site when inserting a sensor ononeself.

The success rate for the initial Sensor Calibration process wasevaluated from results recorded in the receiver log data for eachsuccessful sensor insertion attempt. The time required to complete thefirst sensor calibration was evaluated in addition to the overallsuccess or failure. The percentage of sensors that were successfullycalibrated and produced glucose results within the first 12 hours wascalculated. Sensor calibration is not allowed within the first 10 hours.Sensors that could not be calibrated because conditions were out ofrange were excluded, e.g., if the glucose was changing too rapidly forcalibration. The percentage of sensors that were successfully calibratedwithin 12 hours, when used as directed, was similar for the blinded(90.5%) and unblinded (92.6%) phases of the study (91.5% overall).

Sensor duration was evaluated as the time duration from sensor insertionto the last CM glucose result reported for the sensor. Some sensors wereremoved early by user error or discretion, or because of protocollogistics such as the end of the trial. These sensors are excluded fromanalysis, unless the sensor reached the nominal 5-day sensor life (>108hours). The median sensor life was similar for the blinded (119.9 hours)and unblinded (120.0 hours) phases of the study. The percentage ofsensors, used as directed, that produced glucose results for 108 hoursor more was similar for the blinded (83.5%) and unblinded (83.0%) phasesof the study. Sensors on the arm tended to have slightly longer duration(86.2% for >108 hours) than those on the abdomen (79.4%), because thereis somewhat less flexing and folding of the skin at the posterior arminsertion site than on the abdomen, improving the effectiveness of theskin adhesive that holds the sensor in place.

Glycemic Analysis

The change in glycemic status between the blinded and unblinded phasesof the study was stratified by type 1 and type 2 diabetes. During theunblinded phase when alarms were set, subjects were instructed toperform a BG test when alarms were triggered. Some important differencesin controlling glucose concentration with insulin administration betweenthe two types of diabetes are the following:

-   -   Subjects with type 2 diabetes are less likely to induce        hypoglycemia with insulin because they are insensitive to        insulin. Type 1 subjects, with normal insulin sensitivity are        much more likely to induce hypoglycemia.    -   Subjects with type 2 diabetes can reduce hyperglycemia by        reducing carbohydrate ingestion and allowing endogenous insulin        to reduce blood glucose. Patients with type 1 diabetes produce        no endogenous insulin, so a reduction of carbohydrates is not a        viable strategy for controlling glucose. Controlling glucose        with injected insulin is much more difficult than control with        endogenous insulin.

The time spent in hypoglycemic (<70 mg/dL), euglycemic (70-180 mg/dL)and hyperglycemic ranges is illustrated in FIG. 8 for type 1 and 2subjects in the blinded and unblinded phases of the study.

The type 1 subjects improved in the unblinded phase by reducing time inhypoglycemia. The time spent below the 70 mg/dL threshold forhypoglycemia was reduced by 42% from 1.4 hours to 0.8 hours (p<0.0001).The time spent in hyperglycemia (>180 mg/dL) did not change.

For type 2 subjects, the duration of hyperglycemia improved in theunblinded phase. The time spent in the euglycemic range increased by 12%(p=0.0027) and the time spent >180 mg/dL decreased by 18% (p=0.0057). Asanticipated, the measures of hypoglycemia for type 2 subjects, whichwere low in the blinded phase, were largely unchanged in the unblindedphase.

Accordingly, a continuous analyte monitoring system in one embodimentincludes an analyte sensor having at least about 80% of its paired datapoints within zone A and at least about 95% of its paired data pointswithin zone A and zone B of the Clarke Error Grid, a transmitter capableof receiving information from the sensor, and a receiver capable ofreceiving information from the transmitter.

In one aspect, analyte sensor has at least about 85% of its paired datapoints within zone A of the Clarke Error Grid.

In a further aspect, the analyte sensor has at least about 90% of itspaired data points within zone A of the Clarke Error Grid.

In still a further aspect, the analyte sensor has more thanapproximately 90% of its paired data points within zone A of the ClarkeError Grid.

Additionally, in another aspect, the analyte sensor has at least about85% of its paired data points within zone A of the Consensus Error Grid,and further, where the analyte sensor has at least about 90% of itspaired data points within zone A of the Continuous Glucose Error GridAnalysis.

The analyte sensor may be a glucose sensor.

In yet another aspect, the system may not require confirmation ofanalyte data obtained by the system.

The system may include a drug delivery device, where one or more of thetransmitter and the receiver may be adapted to transmit analyteinformation to the drug delivery device.

In another aspect, the analyte sensor may be calibrated using singlepoint calibration.

A continuous analyte monitoring system in accordance with anotherembodiment includes an analyte sensor having at least about 85% of itspaired data points within zone A and at least about 95% of its paireddata points within zone A and zone B of the Consensus Error Grid, atransmitter capable of receiving information from the sensor, and areceiver capable of receiving information from the transmitter.

The analyte sensor may have at least about 85% of its paired data pointswithin zone A of the Consensus Error Grid.

The analyte sensor may have at least about 90% of its paired data pointswithin zone A of the Consensus Error Grid.

The analyte sensor may have more than approximately 90% of its paireddata points within zone A of the Consensus Error Grid.

In another aspect, the system may not require confirmation of analytedata obtained by the system.

The system may include a drug delivery device, where one or more of thetransmitter and the receiver may be adapted to transmit analyteinformation to the drug delivery device.

Also, the analyte sensor may be calibrated using single pointcalibration.

A method of monitoring glucose levels in accordance with still anotherembodiment includes determining glucose concentration using a firsttranscutaneously positioned analyte sensor, reporting glucoseconcentration to a user, where a second sensor is not used to confirmthe accuracy of the first transcutaneously positioned analyte sensor.

In one aspect, determining may include over a period of time rangingfrom about 1 day to about 7 days.

The first transcutaneously positioned analyte sensor may have at leastabout 85% of its paired data points within zone A of the Clarke ErrorGrid.

The first transcutaneously positioned analyte sensor may have at leastabout 90% of its paired data points within zone A of the Clarke ErrorGrid.

The first transcutaneously positioned analyte sensor may have more thanabout 90% of its paired data points within zone A of the Clarke ErrorGrid.

The first transcutaneously positioned analyte sensor may be a glucosesensor.

The method in a further aspect may include determining health relatedinformation based on the reported glucose concentration, where thehealth related information may include a bolus amount, or one or more ofa food intake, medication dosage level, or activity level.

Also, the medication dosage level may include insulin dosage level.

In a further aspect, the method may include transmitting the reportedglucose concentration, and where transmitting may include one or more ofa wired transmission or a wireless transmission.

In still another aspect, the method may include calibrating the firsttranscutaneously positioned analyte sensor using single pointcalibration.

The first transcutaneously positioned analyte sensor may have at leastabout 95% of its paired data points within zone A and zone B of theClarke Error Grid.

The first sensor may have at least about 85% of its paired data pointswithin zone A.

A method of monitoring glucose levels in accordance with yet anotherembodiment includes determining glucose concentration using a firsttranscutaneously positioned analyte sensor, reporting glucoseconcentration to a user, where accuracy of the first transcutaneouslypositioned analyte sensor is established other than with a secondsensor.

In one aspect, the first transcutaneously positioned analyte sensor hasat least about 85% of its paired data points within zone A of the ClarkeError Grid.

In another aspect, the first transcutaneously positioned analyte sensorhas at least about 90% of its paired data points within zone A of theClarke Error Grid.

In still another aspect, the first transcutaneously positioned analytesensor has more than about 90% of its paired data points within zone Aof the Clarke Error Grid.

The first transcutaneously positioned analyte sensor may be a glucosesensor.

A method of monitoring glucose levels using a single glucose sensor inaccordance with still yet a further embodiment includes transcutaneouslypositioning a glucose sensor in a patient for a period of time,determining glucose concentration of the patient using thetranscutaneously positioned glucose sensor, and using one or moreadditional devices during the period of time only to calibrate theglucose sensor but not to confirm the accuracy of the transcutaneouslypositioned glucose sensor.

The glucose sensor in one embodiment has at least about 85% of itspaired data points within zone A and at least about 95% of its paireddata points within zone A and zone B of the Clarke Error Grid.

The glucose concentration may be determined over a period of timeranging from about 1 day to about 7 days.

In a further aspect, the glucose sensor has at least about 85% of itspaired data points within zone A of the Clarke Error Grid.

In yet another aspect, the glucose sensor has at least about 90% of itspaired data points within zone A of the Clarke Error Grid.

The glucose sensor in still another aspect has more than approximately90% of its paired data points within zone A of the Clarke Error Grid.

In still a further aspect, the method may include determining a healthrelated information based on the determined glucose concentration, wherethe health related information includes one or more of a food intake,medication dosage level, or activity level, and further, wheremedication dosage level includes insulin dosage level.

The method may include transmitting data associated with the determinedglucose concentration, where transmitting may include one or more of awired transmission or a wireless transmission.

Also, calibration of the glucose sensor may include performing singlepoint calibration.

An analyte monitoring system in accordance with still yet anotherembodiment includes an analyte sensor configured to detect one or moreanalyte levels of a patient, a transmitter unit operatively coupled tothe analyte sensor, the transmitter unit configured to transmit one ormore signals associated with the detected one or more analyte levels,and a receiver unit configured to receive the transmitted one or moresignals associated with the detected one or more analyte levels, wherethe accuracy of the detected one or more analyte levels relied upon tomake a clinically relevant decision is established without using a bloodglucose measurement.

In one aspect, the clinically relevant decision may include healthcaredecision.

The clinically relevant decision may include a bolus amountdetermination.

The blood glucose measurement may include a confirmatory blood glucosemeasurement.

The detected one or more analyte level may be calibrated, for example,using single point calibration.

The transmitter unit may be configured to wirelessly transmit the one ormore signals to the receiver unit.

The analyte sensor in one embodiment has at least about 85% of itspaired data points within zone A and at least about 95% of its paireddata points within zone A and zone B of the Clarke Error Grid.

An analyte monitoring device in accordance with still yet a furtherembodiment includes a receiver unit for receiving one or more signalsrelated to an analyte level detected by an electrochemical sensor, thereceiver unit including a display to display an indication of theanalyte level, where the electrochemical sensor has at least about 85%of its paired data points within zone A and at least about 95% of itspaired data points within zone A and zone B of the Clarke Error Grid.

The electrochemical sensor may have at least about 85% of its paireddata points within zone A of the Clarke Error Grid.

The electrochemical sensor may have at least about 90% of its paireddata points within zone A of the Clarke Error Grid.

The electrochemical sensor may have more than approximately 90% of itspaired data points within zone A of the Consensus Error Grid.

The receiver unit may be configured to calibrate the one or more signalsrelated to the analyte level, and further, where the receiver unit maybe configured to display the calibrated one or more signals related tothe analyte level without a confirmatory blood glucose measurement.

In another aspect, the receiver unit may be configured to calibrate theone or more signals related to the analyte level using single pointcalibration.

The receiver unit may be configured to display the one or more signalsrelated to the analyte level without a confirmatory blood glucosemeasurement.

The receiver unit in one embodiment may include one of an rf receiver oran rf transceiver.

The receiver unit in still a further aspect may be configured tocalibrate the one or more signals related to the analyte level using acalibration value of less that about one microliter of body fluid, wherethe body fluid includes blood.

The receiver unit may include an alarm configured to indicate when theanalyte level is at or near a threshold level.

The threshold level may include one of hypoglycemia, impendinghypoglycemia, hyperglycemia, or impending hyperglycemia.

The alarm may include one or more of an audible signal, a visualdisplay, or a vibratory signal.

The alarm may be configured to automatically deactivate after apredetermined time period.

The receiver unit in one aspect may be a portable handheld unit.

The receiver unit may be configured for wearing on or under an articleof clothing.

The receiver unit may include an rf transceiver configured to receive ortransmit the one or more signals related to an analyte level.

In still another aspect, the display may be configured to display one ormore of analyte level trend information, rate of change informationassociated with the analyte level, basal profile information, bolusamount information, or therapy related information.

In a further aspect, the receiver may include a blood glucose meter.

The display may be configured to display the indication of the analytelevel at least one or more of once per minute, once per five minutes,once per ten minutes, or over a predetermined time period, where thepredetermined time period may include one or more of less than 24 hourperiod, one day, three days, seven days, fourteen days, twenty one days,twenty eight days, less than thirty days, or greater than thirty days.

A monitoring device in a further embodiment includes a portable housing,an rf receiver coupled to the portable housing, the rf receiverconfigured to wirelessly receive one or more signals related to ananalyte level of a patient detected by an electrochemical sensor, aprocessing unit coupled to the portable housing and to the rf receiver,the processing unit configured to process the one or more signalreceived by the rf receiver, and a display unit coupled to the portablehousing and the processing unit, the display unit configured to displayan indication associated with the one or more signals related to theanalyte level of the patient, where the electrochemical sensor has atleast about 85% of its paired data points within zone A and at leastabout 95% of its paired data points within zone A and zone B of theConsensus Error Grid.

The electrochemical sensor may have at least about 85% of its paireddata points within zone A of the Consensus Error Grid.

The electrochemical sensor may have at least about 90% of its paireddata points within zone A of the Consensus Error Grid.

An analyte monitoring device in accordance with still another embodimentincludes a receiver unit for receiving one or more signals related to ananalyte level detected by an electrochemical sensor, the receiver unitincluding a display to display an indication of the analyte level, andthe receiver unit further configured to process one or more signalsrelated to analyte related therapy for communication with a drugadministration system, where the electrochemical sensor has at leastabout 85% of its paired data points within zone A and at least about 95%of its paired data points within zone A and zone B of the Clarke ErrorGrid.

In one aspect, the electrochemical sensor has at least about 90% of itspaired data points within zone A of the Clarke Error Grid.

Various other modifications and alterations in the structure and methodof operation of this disclosure will be apparent to those skilled in theart without departing from the scope and spirit of the presentdisclosure. Although the present disclosure has been described inconnection with specific embodiments, it should be understood that theembodiments of the present disclosure as claimed should not be undulylimited to such specific embodiments. It is intended that the followingclaims define the scope of the present disclosure and that structuresand methods within the scope of these claims and their equivalents becovered thereby.

1. A method of monitoring glucose levels, comprising: determiningglucose concentration using a first transcutaneously positioned analytesensor; and reporting glucose concentration to a user; wherein a secondsensor is not used to confirm the accuracy of the first transcutaneouslypositioned analyte sensor.
 2. The method of claim 1, wherein determiningcomprises over a period of time ranging from about 1 day to about 7days.
 3. The method of claim 1, wherein the first transcutaneouslypositioned analyte sensor has at least about 85% of its paired datapoints within zone A of the Clarke Error Grid.
 4. The method of claim 1,wherein the first transcutaneously positioned analyte sensor has atleast about 90% of its paired data points within zone A of the ClarkeError Grid.
 5. The method of claim 1, wherein the first transcutaneouslypositioned analyte sensor has more than about 90% of its paired datapoints within zone A of the Clarke Error Grid.
 6. The method of claim 1,wherein the first transcutaneously positioned analyte sensor is aglucose sensor.
 7. The method of claim 1, comprising determining healthrelated information based on the reported glucose concentration.
 8. Themethod of claim 7 wherein the health related information includes abolus amount.
 9. The method of claim 7 wherein the health relatedinformation includes one or more of a food intake, medication dosagelevel, or activity level.
 10. The method of claim 9, wherein themedication dosage level includes insulin dosage level.
 11. The method ofclaim 1, comprising transmitting the reported glucose concentration. 12.The method of claim 11 wherein transmitting includes one or more of awired transmission or a wireless transmission.
 13. The method of claim 1including calibrating the first transcutaneously positioned analytesensor using single point calibration.
 14. The method of claim 1 whereinthe first transcutaneously positioned analyte sensor has at least about95% of its paired data points within zone A and zone B of the ClarkeError Grid.
 15. The method of claim 14 wherein the first sensor has atleast about 85% of its paired data points within zone A.
 16. A method ofmonitoring glucose levels, comprising: determining glucose concentrationusing a first transcutaneously positioned analyte sensor; and reportingglucose concentration to a user; wherein accuracy of the firsttranscutaneously positioned analyte sensor is established other thanwith a second sensor.
 17. The method of claim 16, wherein the firsttranscutaneously positioned analyte sensor has at least about 85% of itspaired data points within zone A of the Clarke Error Grid.
 18. Themethod of claim 16, wherein the first transcutaneously positionedanalyte sensor has at least about 90% of its paired data points withinzone A of the Clarke Error Grid.
 19. The method of claim 16, wherein thefirst transcutaneously positioned analyte sensor has more than about 90%of its paired data points within zone A of the Clarke Error Grid. 20.The method of claim 16, wherein the first transcutaneously positionedanalyte sensor is a glucose sensor.